
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "140"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 113 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 107 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	3.24%	1.779091	-0.064483	0.129012	-0.374964	-0.125559	-0.430855	-0.353779	-0.250967	0.7883	0.8002	0.2384	3.618102	3.840091
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	0.54%	0.755738	0.161660	-0.878335	-0.313398	1.347989	0.906740	0.034920	0.203021	0.7498	0.7433	0.2454	2.795665	2.880047
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.251368	-0.851853	-0.394211	-1.004357	-0.922328	0.671387	-1.740518	0.010015	0.0367	0.0310	0.0021	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.384978	0.518118	-0.550414	-0.043521	0.827670	1.342474	-0.095129	0.286842	0.6814	0.6923	0.3521	1.607820	1.583810
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.682441	0.671110	-0.530498	0.247250	0.226100	2.736836	-0.025436	0.641724	0.6817	0.6899	0.3474	1.445946	1.390153
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	1.203962	0.540530	-0.217849	0.247944	-0.540193	-0.379947	0.231371	0.188039	0.7193	0.7184	0.2763	1.969729	2.007576
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.54%	2.337401	0.690134	-0.428999	-0.585838	0.878605	0.871514	0.102560	0.186543	0.7903	0.7754	0.2122	4.607259	3.447312
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	354.219782	354.305698	inf	inf	110.228487	106.494813	618.617812	611.786235	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	16.22%	1.62%	0.509858	-0.419627	-0.726366	-0.804711	0.274718	0.678927	0.040429	-0.059551	0.6607	0.6684	0.3507	1.518223	1.503202
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.903385	-1.293839	-1.209301	-1.068277	-0.763270	-0.628360	1.440306	-0.072215	0.0368	0.0448	0.0031	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.349844	2.506277	2.097905	1.600610	1.403696	3.554674	5.562004	6.021384	0.6215	0.6274	0.3436	0.000000	0.000000
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	97.31%	0.00%	1.190161	0.476439	0.012094	0.058203	-0.069688	2.401765	-0.019534	-0.144678	0.6792	0.6879	0.3642	0.000000	0.000000
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	45.41%	0.54%	0.959526	0.939370	0.316908	0.068177	-0.042912	2.243162	0.020377	-0.676383	0.6821	0.6874	0.3644	1.476695	1.464938
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	16.76%	1.468719	1.448506	-0.470622	0.036021	0.073948	2.492426	0.230784	0.919757	0.6896	0.6956	0.3578	2.033392	1.909260
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	99.46%	0.54%	1.210750	0.965833	0.458910	0.098118	-0.107971	2.951427	-0.181085	1.944277	0.6804	0.6867	0.3617	0.000000	0.000000
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	0.761911	0.229713	0.636943	-0.001235	0.152912	1.792807	0.661028	-0.712389	0.6776	0.6895	0.3657	1.509952	1.479214
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.288231	0.362355	-0.464694	-0.039493	1.389647	2.213000	1.100070	-0.217643	0.0392	0.0442	0.0057	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.979720	0.714508	-0.270193	-0.268087	0.153523	0.918003	0.161576	0.007700	0.6802	0.6933	0.3659	1.819704	1.644348
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	8.60%	1.011386	0.388760	-0.399082	-0.565951	-0.307260	0.476319	0.352671	0.116849	0.7039	0.7195	0.3650	1.709761	1.737120
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	24.73%	0.089798	0.162372	-0.196831	-0.366833	-0.673505	0.952134	-0.175094	-0.365250	0.7613	0.7660	0.3113	2.145665	1.963761
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	12.97%	0.888652	0.088153	-0.571591	-0.653394	-0.106080	-0.472006	-0.298112	-0.533161	0.6995	0.7128	0.3658	1.866157	1.773092
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	2.70%	0.963522	0.376595	0.464129	0.524267	-0.108388	0.404882	0.188487	-0.697720	0.7017	0.7157	0.3626	1.569711	1.521633
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	1.62%	1.015432	-0.016576	-0.295094	-0.393576	-0.336820	0.231846	0.828861	-0.843331	0.7081	0.7222	0.3612	1.563183	1.614168
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.052486	0.300965	-0.446960	-0.190206	0.560768	-0.443395	1.008145	-0.255229	0.0329	0.0302	0.0015	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	49.489148	23.487528	22.545799	27.002392	21.415015	1.818943	4.167400	3.662538	0.0588	0.0758	0.0101	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	1.62%	0.509749	0.151394	-0.453335	-0.478259	-0.459041	-0.585047	-0.472919	-0.417432	0.7740	0.7821	0.2288	2.731078	2.334692
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.460670	0.233677	-0.105429	-0.104211	-0.109174	1.163801	0.030383	0.859660	0.6931	0.7066	0.3770	1.513047	1.523489
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	0.176781	0.176781	0.138346	0.138346	0.470698	0.470698	0.005728	0.005728	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	6.94%	0.00%	0.105518	-0.219019	0.080877	0.356956	1.436004	1.485538	0.637322	2.918657	0.7092	0.7120	0.3716	1.805090	1.569176
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	10.75%	0.352903	0.400702	1.062286	1.067676	0.151605	2.471914	0.948256	3.196690	0.7063	0.7128	0.3685	2.101588	1.949889
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	-0.260032	-0.642842	-0.663582	-0.614144	-0.190305	-0.313987	0.586841	0.602234	0.7068	0.7177	0.3649	2.004406	1.755036
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	7.03%	-0.270635	-0.196736	-0.888554	-0.936902	-0.216562	-0.944955	0.317599	-0.443360	0.7089	0.7137	0.3613	1.948183	1.811444
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	7.03%	-0.286422	-0.524379	-0.676105	-0.836954	0.078479	-0.773272	0.634498	0.712183	0.7048	0.7163	0.3732	1.888211	1.724514
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	2.15%	-0.792795	-0.663915	-0.829986	-0.832290	-0.177913	-0.935669	0.297971	0.266234	0.7028	0.7146	0.3662	1.898846	1.690756
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	10.22%	1.164498	0.478313	0.511295	0.140798	-0.119733	1.505164	0.107011	0.602573	0.7065	0.7124	0.3713	1.813145	1.700488
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0450	0.0392	0.0028	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	2.16%	0.668606	0.432793	0.447635	-0.133252	0.205650	1.599837	0.865766	-0.803535	0.7038	0.7132	0.3707	1.785759	1.722836
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7113	0.7221	0.3772	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7060	0.7161	0.3766	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	1.493427	0.933068	0.441865	0.905474	0.369414	-0.189300	0.154562	-0.617496	0.7144	0.7233	0.3685	1.411433	1.507073
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.189972	-0.589101	-0.891206	-0.925692	-0.347067	-0.781892	0.721536	-0.227149	0.7095	0.7213	0.3688	1.850845	1.645696
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0534	0.0511	0.0014	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.851775	0.392245	-0.008345	0.060420	0.069530	1.101618	0.708302	-0.003389	0.7035	0.7163	0.3823	1.963633	1.700758
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	1.79%	0.974736	0.668065	0.377131	1.122891	-0.537056	-0.515618	0.563151	-0.057251	0.7506	0.7579	0.3394	2.060213	1.687032
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	1.137837	0.664882	0.484630	-0.081316	0.022770	-0.967090	0.521303	-0.036756	0.7390	0.7457	0.3126	1.526086	1.512908
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	5.80%	0.326375	-0.161461	-1.029293	-0.967756	-0.451770	0.373525	-0.253427	-0.716782	0.7561	0.7605	0.3257	1.712702	1.604804
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	1.727973	0.585545	0.588871	0.034527	1.304304	0.607831	1.809771	2.202000	0.7336	0.7377	0.3931	1.412133	1.322349
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.023568	-0.379135	-0.788852	-0.902804	-0.596396	0.604412	0.169411	-0.576264	0.7879	0.7853	0.3327	1.746209	1.562639
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.45%	-0.024201	-0.489897	-0.758605	-1.008113	0.108118	1.147108	0.240129	-0.406380	0.7811	0.7716	0.3294	1.848448	1.668000
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	0.880605	0.634337	0.445738	0.552463	-0.273089	-0.204596	1.079502	-0.913741	0.7249	0.7378	0.3819	1.719452	1.570305
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	1.530783	0.848884	0.628559	0.456675	-0.500830	-0.695086	0.155968	-1.044767	0.7318	0.7425	0.3734	1.638398	1.526943
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	5.92%	0.00%	-0.180437	-0.466122	-0.708487	-0.771457	0.018802	-0.490479	0.139967	-0.514723	0.7387	0.7459	0.3793	1.703819	1.588908
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.992742	0.523603	0.274110	0.853521	-0.009353	-0.582393	-0.277493	-0.590126	0.9151	0.9432	0.2831	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	12.24%	6.12%	0.857813	0.921138	-0.255447	0.240658	1.859154	2.183786	0.481864	-0.630510	nan	nan	nan	2.722135	2.367287
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.986816	0.602170	0.568639	0.462971	1.241549	-0.461129	0.500942	-0.175968	0.6884	0.7235	0.3381	1.568021	2.002032
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.059533	0.357134	-0.651468	-0.558657	1.833546	-0.687247	0.840584	2.116522	0.0450	0.0473	0.0072	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	1.030291	0.212636	0.269427	0.337942	0.120335	1.284570	-0.153613	-0.009025	0.9207	0.9505	0.1718	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	2.257622	0.387776	0.609213	-0.669283	0.822719	0.943691	0.749113	0.842487	0.8827	0.8538	0.2010	6.899126	4.166395
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	2.522371	0.893446	1.033282	0.794753	-0.498671	0.082396	-0.571590	0.399154	0.8899	0.8817	0.4076	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 140 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 140, Node 13:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 140: 2459642

Antenna 140: 2459640

Antenna 140: 2459634

Antenna 140: 2459632

Antenna 140: 2459631

Antenna 140: 2459630

Antenna 140: 2459629

Antenna 140: 2459628

Antenna 140: 2459627

Antenna 140: 2459626

Antenna 140: 2459625

Antenna 140: 2459624

Antenna 140: 2459623

Antenna 140: 2459622

Antenna 140: 2459621

Antenna 140: 2459620

Antenna 140: 2459619

Antenna 140: 2459618

Antenna 140: 2459617

Antenna 140: 2459616

Antenna 140: 2459615

Antenna 140: 2459614

Antenna 140: 2459613

Antenna 140: 2459612

Antenna 140: 2459611

Antenna 140: 2459610

Antenna 140: 2459609

Antenna 140: 2459608

Antenna 140: 2459607

Antenna 140: 2459605

Antenna 140: 2459604

Antenna 140: 2459603

Antenna 140: 2459602

Antenna 140: 2459598

Antenna 140: 2459597

Antenna 140: 2459596

Antenna 140: 2459595

Antenna 140: 2459594

Antenna 140: 2459593

Antenna 140: 2459592

Antenna 140: 2459591

Antenna 140: 2459590

Antenna 140: 2459589

Antenna 140: 2459588

Antenna 140: 2459587

Antenna 140: 2459586

Antenna 140: 2459585

Antenna 140: 2459584

Antenna 140: 2459583

Antenna 140: 2459582

Antenna 140: 2459581

Antenna 140: 2459580

Antenna 140: 2459579

Antenna 140: 2459578

Antenna 140: 2459577

Antenna 140: 2459576

Antenna 140: 2459575

Antenna 140: 2459574

Antenna 140: 2459573

Antenna 140: 2459572

Antenna 140: 2459571

Antenna 140: 2459570

Antenna 140: 2459569

Antenna 140: 2459566

Antenna 140: 2459565

Antenna 140: 2459564

Antenna 140: 2459563

Antenna 140: 2459562

Antenna 140: 2459561

Antenna 140: 2459560

Antenna 140: 2459559

Antenna 140: 2459558

Antenna 140: 2459556

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶